Effects of measurement uncertainty on air quality summary statistics |
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| Institution: | 1. Department of Pediatrics, Section of Adolescent Medicine, Indiana University School of Medicine, Indianapolis, Indiana;2. Department of Sociology, Indiana University Purdue University-Indianapolis, Indianapolis, Indiana;3. Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana;4. Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana;5. National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland;1. Department of Urology, Rennes University Hospital, Rennes, France;2. French Referral Network of Spina Bifida, Rennes University Hospital, Rennes, France;3. Department of Urology, Nantes University Hospital, Nantes, France;4. Department of Urology, Toulouse University Hospital, Toulouse, France;5. Department of Physical Medicine and Rehabilitation, Kerpape Hospital, Ploemeur, France;1. Department of Obstetrics and Gynecology, University of Calgary, Calgary, AB, Canada;2. Department of Pediatric and Adolescent Gynecology, Alberta Children''s Hospital, Calgary, AB, Canada;1. Division of Pediatric Urology, Riley Hospital for Children at IU Health, Indianapolis, IN, USA;2. Department of Pediatrics and Department of Sociology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA |
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| Abstract: | This paper examines the effects of measurement uncertainty on various summary statistics that are routinely used in air quality data analysis. Analytical approximations and computer simulation techniques are employed to illustrate and quantify how the uncertainty associated with an individual measurement results in an uncertainty for different summary statistics. Measurement uncertainty may be viewed as consisting of bias and imprecision. It is shown that even when there is no bias for individual measurements it is possible for imprecision alone to result in bias for certain commonly used summary statistics. Different types of statistics are shown to be less influenced by measurement imprecision and, consequently, a data set may be acceptable for some purpose but not for others. The desired precision of the summary statistic may be viewed as a guide in determining an acceptable level of imprecision for individual measurements. |
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